Deniz Ekin Yavas
2023
Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach
Deniz Ekin Yavas
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Laura Kallmeyer
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Rainer Osswald
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Elisabetta Jezek
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Marta Ricchiardi
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Long Chen
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
Identifying semantic argument types in predication contexts is not a straightforward task for several reasons, such as inherent polysemy, coercion, and copredication phenomena. In this paper, we train monolingual and multilingual classifiers with a zero-shot cross-lingual approach to identify semantic argument types in predications using pre-trained language models as feature extractors. We train classifiers for different semantic argument types and for both verbal and adjectival predications. Furthermore, we propose a method to detect copredication using these classifiers through identifying the argument semantic type targeted in different predications over the same noun in a sentence. We evaluate the performance of the method on copredication test data with Food•Event nouns for 5 languages.
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